Military unmanned aerial vehicles (UAVs) perform missions in airspace where one of the mission goals may be radar-detection avoidance. The research conducted aimed at determining optimum flight-path routes that make maximum utilization of UAV terrain masking opportunities and flight range capability in avoiding radar detection. The problem was formulated as one of constrained optimization in three dimensions and advantageous solutions were identified using Algorithm A*. The study conducted extended the substantial existing literature on radar-detection avoidance for UAVs in three significant ways:
First, it explicitly modeled prominent terrain cover, such as forests and urban areas, in masking radar detection through the adaptation of a ray-casting technique in order to determine if expanded flight path cells were visible to any nearby radar. Any existing terrain cover masking prospects were exploited by the algorithm to avoid radar detection, in addition to prospects arising for terrain masking through topographical features.
Second, it explicitly modeled the trade-off between detection risk and flight-path length by using a weighted average of the total flight-path length and the radar detectable flightpath length as the objective function to be minimized. The relative importance of detection risk and flight-path length is captured by adjusting the weights.
Third, a mission planning process was obtained allowing route selection based on the characteristics of a portfolio of parameters and options, including radar detection exposure, the total distance traveled on a respective flight path, and a computed missile shoot-down risk benchmark for each option. The benchmark estimates the likelihood of
aircraft destruction from missile impact subsequent to an integrated air defense system detection, acquisition, tracking, and missile launch sequence.
The model was further extended to include provisions for preferred altitude ranges, adjustable aircraft climb and descent rate envelopes, fractional detection probability based movement costs (accounting for simultaneous detection by multiple radars), radar horizon masking, and a perspective based ellipsoidal radar cross section model. Resulting routes can be executed at a significantly lower expected cost than the alternative of using a human piloted stealth aircraft that may be required for a similar mission in the absence of intelligent route-planning.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1271 |
| Date | 01 January 2010 |
| Creators | Pelosi, Michael Joseph |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | campusdissertation |
| Format | application/pdf |
| Source | CCE Theses and Dissertations |
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